from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.567	0.0	-1	1	NaN	NaN	0.058	0.000	0.211	0.211	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.731	0.0	-1	5	NaN	1.000	0.009	0.000	1.364	1.365	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.013	0.0	NaN	NaN	6.056	0.0	1	100	NaN	0.875	0.206	0.002	0.064	0.064	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.829	0.0	-1	100	NaN	NaN	0.047	0.000	0.251	0.251	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.665	0.0	1	5	NaN	1.000	0.001	0.000	16.947	17.281	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.896	0.0	1	1	NaN	0.975	0.060	0.001	0.195	0.195	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	NaN	0.352	0.0	-1	1	NaN	NaN	0.009	0.000	0.526	0.526	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	NaN	0.355	0.0	-1	5	NaN	1.000	0.001	0.000	6.022	6.245	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.904	0.015	NaN	NaN	0.000	0.017	-1	1	0.663	0.687	0.170	0.000	99.719	99.719	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.875	0.022	NaN	NaN	0.000	0.002	-1	1	0.663	1.000	0.009	0.000	215.903	216.084	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.297	0.007	NaN	NaN	0.000	0.297	-1	1	1.000	NaN	0.046	0.000	6.395	6.395	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.025	0.003	NaN	NaN	0.000	0.025	-1	1	1.000	0.742	0.172	0.002	0.146	0.146	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.820	0.037	NaN	NaN	0.000	0.017	-1	5	0.757	NaN	0.046	0.000	363.376	363.376	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.739	0.033	NaN	NaN	0.000	0.003	-1	5	0.757	0.875	0.206	0.001	13.311	13.311	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.301	0.008	NaN	NaN	0.000	0.301	-1	5	1.000	0.000	0.009	0.000	34.300	34.321	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.025	0.003	NaN	NaN	0.000	0.025	-1	5	1.000	NaN	0.046	0.000	0.547	0.547	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.926	0.031	NaN	NaN	0.000	0.017	1	100	0.882	0.000	0.009	0.000	1923.682	1925.070	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.029	0.002	NaN	NaN	0.000	0.002	1	100	0.882	NaN	0.047	0.000	43.623	43.623	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.303	0.006	NaN	NaN	0.000	0.303	1	100	1.000	0.742	0.171	0.001	1.770	1.770	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.003	NaN	NaN	0.000	0.022	1	100	1.000	1.000	0.009	0.000	2.491	2.492	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.874	0.018	NaN	NaN	0.000	0.017	-1	100	0.882	0.687	0.169	0.000	99.831	99.831	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.727	0.031	NaN	NaN	0.000	0.003	-1	100	0.882	1.000	0.009	0.000	315.711	316.042	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.315	0.006	NaN	NaN	0.000	0.315	-1	100	1.000	NaN	0.008	0.000	37.077	37.077	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.002	NaN	NaN	0.000	0.024	-1	100	1.000	0.967	0.025	0.000	0.947	0.947	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.806	0.025	NaN	NaN	0.000	0.017	1	5	0.757	NaN	0.009	0.000	1947.692	1947.692	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.024	0.004	NaN	NaN	0.000	0.002	1	5	0.757	0.974	0.027	0.000	76.131	76.132	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.305	0.005	NaN	NaN	0.000	0.305	1	5	1.000	1.000	0.001	0.000	421.611	428.248	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.021	0.000	NaN	NaN	0.000	0.021	1	5	1.000	NaN	0.009	0.000	2.429	2.429	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.224	0.061	NaN	NaN	0.000	0.017	1	1	0.663	1.000	0.001	0.000	21372.350	21612.701	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.174	0.008	NaN	NaN	0.001	0.001	1	1	0.663	NaN	0.009	0.000	135.927	135.927	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.303	0.005	NaN	NaN	0.000	0.303	1	1	1.000	0.975	0.059	0.000	5.103	5.103	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.020	0.000	NaN	NaN	0.000	0.020	1	1	1.000	1.000	0.001	0.000	25.311	25.829	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	3.703	0.003	NaN	NaN	0.000	0.004	-1	1	0.896	0.974	0.027	0.003	134.810	135.414	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	1.726	0.012	NaN	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.000	2372.856	2402.700	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.239	0.004	NaN	NaN	0.000	0.239	-1	1	1.000	NaN	0.009	0.000	27.770	27.770	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.007	0.003	NaN	NaN	0.000	0.007	-1	1	1.000	0.967	0.025	0.000	0.287	0.287	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	2.847	0.0	NaN	NaN	0.028	0.0	-1	1	NaN	NaN	0.791	0.000	3.600	3.600	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.541	0.0	NaN	NaN	0.023	0.0	-1	5	NaN	1.000	0.000	0.000	8063.217	8996.895	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.552	0.0	NaN	NaN	0.023	0.0	1	100	NaN	0.940	0.617	0.005	5.755	5.755	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.581	0.0	NaN	NaN	0.022	0.0	-1	100	NaN	NaN	0.702	0.000	5.102	5.102	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.656	0.0	NaN	NaN	0.022	0.0	1	5	NaN	1.000	0.000	0.000	31067.832	39240.289	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.614	0.0	NaN	NaN	0.022	0.0	1	1	NaN	0.917	0.005	0.000	774.527	776.517	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.003	0.0	NaN	NaN	0.006	0.0	-1	1	NaN	NaN	0.003	0.000	0.906	0.906	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	NaN	0.024	0.0	-1	5	NaN	1.000	0.000	0.000	5.891	7.502	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	111.325	0.000	NaN	NaN	0.000	0.111	-1	1	0.929	0.910	0.114	0.003	979.420	979.757	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.472	0.007	NaN	NaN	0.000	0.000	-1	1	0.929	1.000	0.000	0.000	1603.727	1790.399	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.558	0.061	NaN	NaN	0.000	2.558	-1	1	1.000	NaN	0.717	0.000	3.566	3.566	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.000	NaN	NaN	0.000	0.003	-1	1	1.000	0.941	0.211	0.013	0.012	0.012	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	108.213	0.000	NaN	NaN	0.000	0.108	-1	5	0.946	NaN	0.723	0.000	149.666	149.666	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.893	0.006	NaN	NaN	0.000	0.001	-1	5	0.946	0.940	0.606	0.007	1.473	1.473	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.582	0.093	NaN	NaN	0.000	2.582	-1	5	1.000	1.000	0.001	0.000	3151.138	3414.864	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	NaN	0.000	0.003	-1	5	1.000	NaN	0.723	0.000	0.004	0.004	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	110.673	0.000	NaN	NaN	0.000	0.111	1	100	0.951	1.000	0.001	0.000	135396.155	146750.426	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	5.156	0.010	NaN	NaN	0.000	0.005	1	100	0.951	NaN	0.709	0.000	7.268	7.268	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.584	0.074	NaN	NaN	0.000	2.584	1	100	1.000	0.941	0.211	0.001	12.227	12.227	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	NaN	0.000	0.003	1	100	1.000	1.000	0.000	0.000	7.266	7.989	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	121.503	0.000	NaN	NaN	0.000	0.122	-1	100	0.951	0.910	0.117	0.002	1039.225	1039.386	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	2.952	0.012	NaN	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	10161.789	11270.864	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.590	0.094	NaN	NaN	0.000	2.590	-1	100	1.000	NaN	0.001	0.000	2714.924	2714.924	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.005	0.001	NaN	NaN	0.000	0.005	-1	100	1.000	0.879	0.000	0.000	10.491	10.903	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	121.827	0.000	NaN	NaN	0.000	0.122	1	5	0.946	NaN	0.003	0.000	48544.851	48544.851	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.530	0.005	NaN	NaN	0.000	0.002	1	5	0.946	0.905	0.001	0.000	1904.947	1947.084	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.586	0.075	NaN	NaN	0.000	2.586	1	5	1.000	1.000	0.000	0.000	22963.682	30156.187	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.002	0.000	NaN	NaN	0.000	0.002	1	5	1.000	NaN	0.004	0.000	0.421	0.421	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	124.189	0.000	NaN	NaN	0.000	0.124	1	1	0.929	1.000	0.000	0.000	1040694.147	1263341.597	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.815	0.006	NaN	NaN	0.000	0.001	1	1	0.929	NaN	0.005	0.000	173.847	173.847	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.584	0.104	NaN	NaN	0.000	2.584	1	1	1.000	0.917	0.005	0.000	560.283	561.881	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	8.011	10.110	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.038	0.001	NaN	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	52.314	53.485	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.021	0.001	NaN	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	184.123	236.107	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.005	0.000	NaN	NaN	0.000	0.005	-1	1	1.000	NaN	0.005	0.000	1.005	1.005	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.002	0.000	NaN	NaN	0.000	0.002	-1	1	1.000	0.879	0.000	0.000	4.582	4.746	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.679	0.0	30	NaN	0.024	0.0	random	NaN	30	NaN	0.388	0.0	1.751	1.751	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.551	0.0	30	NaN	0.029	0.0	k-means++	NaN	30	NaN	0.430	0.0	1.281	1.281	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	5.760	0.0	30	NaN	0.139	0.0	random	NaN	30	NaN	2.719	0.0	2.118	2.118	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	5.780	0.0	30	NaN	0.138	0.0	k-means++	NaN	30	NaN	2.814	0.0	2.054	2.054	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	NaN	0.011	0.000	random	0.000	30	0.001	0.0	0.0	8.897	10.623	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	NaN	0.000	0.001	random	1.000	30	1.000	0.0	0.0	10.537	12.859	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	NaN	0.012	0.000	k-means++	0.001	30	0.001	0.0	0.0	8.861	10.789	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	NaN	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.948	13.444	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	NaN	0.477	0.000	random	0.002	30	0.002	0.0	0.0	5.593	6.215	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	NaN	0.001	0.001	random	1.000	30	1.000	0.0	0.0	10.395	12.724	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	NaN	0.448	0.000	k-means++	0.002	30	0.002	0.0	0.0	6.582	7.354	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	NaN	0.001	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.347	12.556	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.073	0.0	20	NaN	0.002	0.0	random	NaN	20	NaN	0.043	0.0	1.705	1.705	See	See
3	KMeans_short	False	fit	10000	10000	2	0.214	0.0	20	NaN	0.001	0.0	k-means++	NaN	20	NaN	0.080	0.0	2.668	2.668	See	See
6	KMeans_short	False	fit	10000	10000	100	0.453	0.0	20	NaN	0.018	0.0	random	NaN	20	NaN	0.113	0.0	4.000	4.000	See	See
9	KMeans_short	False	fit	10000	10000	100	0.556	0.0	20	NaN	0.014	0.0	k-means++	NaN	20	NaN	0.309	0.0	1.800	1.800	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.002	0.000	20	NaN	0.009	0.000	random	0.002	20	0.001	0.001	0.0	3.131	3.282	See	See
2	KMeans_short	False	predict	10000	1	2	0.007	0.012	20	NaN	0.000	0.007	random	1.000	20	1.000	0.000	0.0	49.869	61.145	See	See
4	KMeans_short	False	predict	10000	1000	2	0.004	0.007	20	NaN	0.004	0.000	k-means++	0.002	20	0.001	0.001	0.0	8.252	8.444	See	See
5	KMeans_short	False	predict	10000	1	2	0.003	0.005	20	NaN	0.000	0.003	k-means++	1.000	20	1.000	0.000	0.0	25.456	31.117	See	See
7	KMeans_short	False	predict	10000	1000	100	0.002	0.000	20	NaN	0.325	0.000	random	0.321	20	0.311	0.001	0.0	2.275	2.307	See	See
8	KMeans_short	False	predict	10000	1	100	0.001	0.000	20	NaN	0.001	0.001	random	1.000	20	1.000	0.000	0.0	8.539	9.984	See	See
10	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	NaN	0.318	0.000	k-means++	0.381	20	0.286	0.001	0.0	2.511	2.547	See	See
11	KMeans_short	False	predict	10000	1	100	0.002	0.000	20	NaN	0.001	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.019	10.596	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	11.692	0.0	[20]	NaN	0.068	0.000	NaN	NaN	NaN	NaN	NaN	1.990	0.0	5.874	5.874	See	See
3	LogisticRegression	False	fit	1000	1000	10000	2.455	0.0	[26]	NaN	0.033	0.002	NaN	NaN	NaN	NaN	NaN	0.894	0.0	2.746	2.746	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.000	0.0	[20]	NaN	2.457	0.0	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.953	1.044	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	NaN	0.012	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.373	0.437	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	NaN	5.138	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.0	0.530	0.535	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	NaN	0.922	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.123	0.127	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.182	0.0	NaN	NaN	0.440	0.0	NaN	NaN	NaN	0.182	0.0	0.998	0.998	See	See
3	Ridge	False	fit	1000000	1000000	100	1.490	0.0	NaN	NaN	0.537	0.0	NaN	NaN	NaN	0.562	0.0	2.651	2.651	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.011	0.0	NaN	NaN	7.045	0.0	NaN	NaN	0.083	0.019	0.001	0.602	0.602	See	See
2	Ridge	False	predict	1000	1	10000	0.000	0.0	NaN	NaN	1.014	0.0	NaN	NaN	NaN	0.000	0.000	0.716	0.856	See	See
4	Ridge	False	predict	1000000	1000	100	0.000	0.0	NaN	NaN	5.674	0.0	NaN	NaN	1.000	0.000	0.000	0.631	0.726	See	See
5	Ridge	False	predict	1000000	1	100	0.000	0.0	NaN	NaN	0.013	0.0	NaN	NaN	NaN	0.000	0.000	0.680	0.853	See	See